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- 6 hard quiz - questions with 4-6 T/F options (not necessarily from your everyday ML algorithms). - 1 medium leetcode problem (from their list of most repeated questions) - 2 implementation questions. Mine were KNN and Kmeans. - One neural network hand-calculation (easy) Overall the assessment was thorough, but the time limit is unreasonably low! As someone with tons of coding and ML experience, I could say you had to be top 10% of Stanford graduates so that you can come close to getting all the questions right in 70 mins.
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ML Engineer

Interviewed at Pinterest

3.6★
Nov 18, 2022

- 6 hard quiz - questions with 4-6 T/F options (not necessarily from your everyday ML algorithms). - 1 medium leetcode problem (from their list of most repeated questions) - 2 implementation questions. Mine were KNN and Kmeans. - One neural network hand-calculation (easy) Overall the assessment was thorough, but the time limit is unreasonably low! As someone with tons of coding and ML experience, I could say you had to be top 10% of Stanford graduates so that you can come close to getting all the questions right in 70 mins.

First Round: With one interviewer, I was asked a set of Python MCQs covering concepts like mutable vs immutable types, list comprehensions, and variable scope. Additionally, I solved DSA problems focused on arrays and strings to demonstrate my problem-solving skills and optimization techniques. Second Round: Again, with a single interviewer, I worked on machine learning coding problems and discussed core ML concepts, including algorithms, model evaluation, and practical ML applications.
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ML Scientist

Interviewed at Lytx

3.7★
May 26, 2025

First Round: With one interviewer, I was asked a set of Python MCQs covering concepts like mutable vs immutable types, list comprehensions, and variable scope. Additionally, I solved DSA problems focused on arrays and strings to demonstrate my problem-solving skills and optimization techniques. Second Round: Again, with a single interviewer, I worked on machine learning coding problems and discussed core ML concepts, including algorithms, model evaluation, and practical ML applications.

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